The information economy as a rod. The world is teaming with innovation as new
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and inside Analysis dot Comside Analysis dot com. And now here's your host,
Eric Kavanaugh. Yes, oh, yes, players and gentlemen's hello and welcome
back once again to the only coast to coast radio show that's all about the
information economy. It's called Inside Analysis. Yours truly, Eric Kavanaugh here with
a very special show today, folks, and a very special guest. We're
gonna be talking to Bob Muglia, the former CEO of Snowflake and an all
around innovator in our industry for many, many years. And he's got a
new book along with Steve Hamm, a book called The Data Preneurs, The
Promise of AI and the Creators Building our Future. And you know, I'm
sitting here in Gibsonia, Pennsylvania, and I'm inclined to come up with a
great quote from one of my personal heroes, someone I follow and learn from,
William Gibson, who was a futurist and he had a great quote where
he said the future is here already, it's just not evenly distributed, and
I was like, Holy Christmas, what kind of a quote is that?
And what he means is that there are pockets of innovation. There are pockets
like in cities, for example, where you have more cell phones and cell
towers and five G more laptops and iPads and things of this nation. And
you go way out of the country and maybe not so much. You have
more factory equipment, for example, or mills or mines or different things of
this nature, and it's a different story, but you do have these pockets
of innovation. Of course, it also means the past is all around us
as well, right it's not just the future. But what's cool about the
time we're in right now is that this is a very significant changing of the
guard, if you will. You look at what artificial intelligence is now doing,
and it's not new. We've had AI for forty fifty years. The
term has been around for a long time. The algorithms, some of them
have been around for a long time. But what we didn't have was the
compute. But we didn't have was the data to train these algorithms and we
didn't have the force of business to push it through, and now we certainly
do so we've kind of seen these worlds of data management and artificial intelligence and
machine learning really start to coalesce in the last few years. And then,
of course chat GPT rolled out their large language model, which is blowing minds,
and then a couple of companies did too. Google even Data Bricks rolled
out a large language model in fairly short order after chat GBT hit the scene.
And there were some strange things in the news, like The New York
Times having a reporter talked to the chat GBT and it told him to leave
his wife and all this crazy stuff. But I would argue that was a
miss use of the technology. You shouldn't be trying to tease weird answers out
of a large language model. It's there to reflect back the knowledge that is
extant in the world today, and it can do so in a grammatically correct,
syntactically compelling sort of way. It can write proposals, it can write
tweets, it can write articles and blogs and all sorts of different things.
It does hallucinate, it does come up with stuff because it's really just parsing
little fragments of content that it's found somewhere else on the web. So there
are lots of interesting things to get into around all that. But today we
wanted to help Bob promote his new book. I've really learned about the impetus
behind this and understand more about what makes Bob ticks. So with that,
Bob mowgli All, welcome to Inside Analysis. Congrats on your book. I
think it's very exciting. I'm looking over it over the weekend. It must
feel good to put that to bed and begin the promotion process, right,
It does feel good. It's been a lot of work. I've been working
on it for a couple of years. When Steve and I started on this,
the really idea was is that I had some things I wanted to say.
I thought there's some things I could help teach people from my career,
and I wanted to say it in a way that was easy for people to
read and enjoyable. And so we came up with the idea of talking about
technology and the advances in data from the perspective of the entrepreneurs of data,
the people who have made it happen really over the last forty years. And
I've had the incredible fortune to be able to work with quite a number of
these folks, and so we wanted to highlight those folks, the so called
datapreneurs. We're able to quickly shorten the name from the data entrepreneurs to the
datapreneurs a little bit catch here. I hope to highlight their work starting,
you know, really in the early days of Sequel, the early days of
databases, going through the Internet era, the work that was done at Microsoft
around information at your fingertips, and then all of the advancements that we've seen
in the modern data stack in the last ten years. And when we started
the book, really we knew that, you know, I knew that machine
learning was making a significant amount of changes in the world. I saw that
it was having an impact. It was working with new types of data that
we couldn't work on before. For example, images or videos are it was
always data that was relatively opaque to computer's hard to work with. All of
a sudden, this machine learning was beginning to crack it. But we really,
I did not realize how fast this stuff was going to advance. To
be honest, I was, I think, like most of the colleagues I
know, have been caught a little bit flat floated by the speed of advancement
that we've seen in the last eighteen months. And I continue to be kind
of blown away by how fast the innovation is happening right now. Well,
sure, and you know what's really cool here, and I'll try to keep
the focus on the people and the technology, is that people use technology right
there. I look at the media in general and the mainstream media, and
I'm a huge media fan. I'm in the media. Obviously it can learn
a lot from the media. But I've got one of these mantras I throw
out there to people that the narrative is always wrong. And what I mean
by that is the narrative, by definition, is a story, and so
it's going to have some flare to it and some embellishment, for example.
But for some reason the narrative just gets off track and it's hard to get
it back on track. And what I'm talking about here is the narrative that
AI is the red eyed robot that's going to take over the world and all
this stuff, and that's just not the case. I mean, I don't
think these large language models are going to take over the world anytime soon.
But The key is to understand the technology and know how to use it,
know how to leverage it for your business, and that's where the magic happens.
So just this morning, I was talking to one of our partners,
Eve Mulgars from seven W Data. You've met him I form one of our
webinars, and he said, what he's learned about CHAT, GBT, these
large language models is you have to be very explicit when telling it what you
want. So it's just like a requirements documentary kind of going through the process
telling you I want A but not B and not C, but D and
not and not F and give it to me in this style. And the
closer you can get to articulating the policy if you will, which is the
prompts, the better results you're going to get from the technology. And that's
true across the board. Understanding how to use the technology and then doing it
correctly, that's what leads to big success, right absolutely, and right now
people are doing a lot of that prompt engineering to help to get the more
advanced models like GPT four, GPT three five to do exactly what they want.
And we'll see more of that over time as these things become more sophisticated.
The newer models are starting to do things like allowing you to begin to
reason. They actually have reasoning capabilities. It is pretty amazing to start to
see the computers can do something. There's a higher order intelligence that has always
been reserved for humans, and even starting to be able to do simple planning.
The planning isn't its advanced as it will be in the next few years,
but they're starting to do that. The other thing that's fascinating, and
this is I mean, I think is one of the most incredible things that's
happened in the past two months is there's a veritable Cambrian explosion of open source
development happening, with people really all across the world contributing to improve the algorithmic
structure of models to allow allow models to run on much smaller computers to be
inferenced without having these big data centers behind them, so they could actually run
in portable systems, being able to run with much less memory and much and
use a lot less power and just be much more efficient in general. Now
those models are not yet as sophisticated as the bespoke models like a GPT four
is, but the improvement that we've seen in the last three months is pretty
remarkable, and a lot of organed companies are taking advantage of this. One
of the things that I think has been answered, and this was not clear
like four or five months ago, is you know, will this technology be
something that is available? Is it a new digital divide going to be created
with this technology really only be available to the elite few. And I think
it's pretty clear that the cost of intelligence is going to rapidly move to is
close to zero. The cost of this artificial intelligence will go down substantially,
so it will be accessible to pretty much everybody, which I think is incredibly
good. I think it's I was worried if we were going to be in
a situation where the development of these large language models and advanced artificial intelligence was
going to be kept within a handful of companies. Sure, I think it's
far far better if we have a large number of these things being created.
You know, it's funny you should say all that, because I'm always trying
to explain things to my wife and friends and other folks, and I brought
up exactly what you just said. I said, the one danger would be
if there is some way that a handful of corporations which have access to these
technologies, think Quantum, think IBM with access to these quantum machines, and
something a lot of people don't understand is that's going to be a very,
very disruptive development when those machines go mainstream, right, or at least theoretically
they will be. I was talking to a guy from Harmon Cardony of the
Day at the click World conference, and he was pointing out that when this
stuff goes mainstream, when quantum goes mainstream, all of the cryptography falls sideways,
like you'll be able to hack into any kind of system anywhere. So
well, what does that mean? But I think you're right that it is.
And open source is the reason why I'm a huge open source fan.
From the moment I learned what it was about, I did a whole bunch
of research. This is back in two thousand and five, and I remember
at the time, the Apache web server had just eclipsed the Internet Explorer whatever
it was called. The Microsoft oh I was called Internet Explore. It was
called Internet Explorer, Okay, So I asked, I asked, Internet Information
Server was web server? That's right? And Apache passed them, And I
thought to myself, and at the time, I was studying service oriented architecture
and thinking to myself, wait a minute, the nexus of open source as
a methodology, as a discipline and soa this spells trouble for the traditional cloe
source big box vendors like the Oracles and the SAPs of the world's going to
shake out of all this, And it took about nine years longer than I
thought. But then all of a sudden I looked around and there are all
these open source vendors everywhere. There's a whole stack built for data. Of
course, the whole had dupe thing we've talked about in the past, and
of Apache, Spark and all these different things, and it really fundamentally changed
the game about how software is developed and produced and managed. And I think
it became a much more collegial environment, which is good for all of us.
What do you think, Well, I'm a huge fan of open source.
It's interesting because my background, of course, was a Microsoft background,
and certainly in the old days, Microsoft wasn't that open to open source.
That was certainly true, although I felt like I was always on the other
side of that of that particular battle. That was one that Stephen Bill held
fairly close to They had some very strong feelings about rietary software, and I
think that was expressed in number of times. I was generally speaking on the
other side of that, although I built You know, it was funny because
you mentioned information Server, that was my product, and I watched it get
killed, largely because we had a security vulnerability that just you could see.
You could see the trajectory totally changed when Microsoft was attacked, Our software was
attacked, and how all of a sudden it became something that almost nobody wanted
to use at that point. But the general trend towards the Internet using open
source was one that was inexorable, and certainly we've seen it took Microsoft a
long time, for example, to give up their proprietary browser and moved to
Chromium as at as an open source browsing alternative. You know, the engine
inside Edge is now essentially the same engine it's inside Chrome, and that just
means that open source has won that battle. It's interesting the language battle is
not yet understood. I don't think that there is clarity in the industry is
what's going to happen. Certainly, if you talk to the Microsoft folks or
the open AI folks, they will continue to say that that building bespoke sort
of proprietary models that are very that are built with very very large amounts of
compute, that that's going to continue to be a way of staying ahead.
I think they still they believe that, they certainly have believed that, and
certainly they've achieved They've achieved the pre eminent model through that approach um Opening Eye
has demonstrated that, uh that larger that they're building very large models. You
know, we you know, to a GPT four roughly we don't know exactly,
but roughly a trillion parameters in that model. It can take on and
do some things that that that other models can't do. But it's fascinating because
the techniques that are being developed are really quite significant in the open source community,
and I think they're going to have to have an impact on the Googles
and the Opening Eyes and the Metas of the world as they continue to develop
their large their large models. Having having a big companies spend money to on
compute to build the fundamental language foundation in these models is going to be somewhat
necessary for a while. There is open source solutions being built just just open
sourcing all the data and everything else. Um, But I do think there'll
be a balance of these things. Meta did some really good things by by
releasing their model to the to the research community, and then everything wound up
leaking to the open source community, and essentially that's what really set off this
this advancement in the open source world. But again, I think it's a
great thing. I think it's a great thing to see lots of models developing.
As I work with companies that are that are innovating in the artificial intelligence
natural language space, you know, I do see people using a mixture of
these models. GPT four is still the best if you're working with more complex
sorts of questions, but if you if you're the problem you're trying to solve
this more specific these these open source models are actually achieving equal quality to GPT
for it in more isolated cases, and they do so at a fraction of
the compute compute costs, which means that they're cheaper to run and just generate
less carbon. These models could generate a lot of carbon potentially, they're they're
using a lot of compute horsepower right now. Well. And one nice thing
about open source is that we can all collaborate on building foundations such that individual
companies and people can then finish off their last model, which is what makes
their company special. And if you collaborate around the foundation, you can tackle
a lot of really basic issues that become incumferences otherwise, like just being able
to move data from place to place, being able to connect to the networking
side of equations. There are so many foundational components to compute, which,
if standardized and open sourced, will allow us to really focus on that last
mile of innovation that makes us special. What do you think? I totally
agree, And again I think we're seeing that happen given the advancement in the
very rapid innovation that's happened literally since February. I mean we're just talking three
months, it's ridiculous, and seeing the sets of fundamental changes that are coming
out and you know, reducing the time to train. I mean, give
you an example. One of the companies I work with, Documgami is uses
their own model to pull data out of business contracts and for them and their
customers, they want to they don't want to send their customers don't want to
send all the data up to open AI and have it run through GPT four.
They want to keep that more proprietor and they also don't want to deal
with any kind of hallucinations on this. So the models, the models that
they're using are are tuned and focused on working directly with customer information exclusively and
focused on only pulling data out of the customer documents and the way they don't
imagine something. They don't make anything up but the accurate. But they're able.
They were able to train a model that for some of their use cases
it met or exceeded GPT four A capability and it was under ten thousand dollars
in it. Called fine tuning costs, this is where you take a model
that has been already trained, an existing open source model, and then you
fine tune it with you expose it to the documents that are very specific to
the industry that you're head. And so just for a small, relatively incremental
cost to build something at pretty high accuracy rates, Yeah, that's a really
big deal. And once again we're talking about opening the kimono, if you
will, showing people what's going on, allowing them to collaborate around different component
parts of the stack and that's what you get in the open source world.
You have all these projects that do specific things. And of course it started
with Linux, the operating system climbed up the stack. Now we have all
sorts of things, a patchie Spark, for example, a patchy kafta,
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four. Welcome back to Inside Analysis. Here's your host, Eric Tabanaugh.
All right, folks, back here on Inside Analysis. Yes, take us
to the future. Indeed, that's what AI is doing right now. It's
taking us to the future, a future in which we can get a lot
more done. This is a new tool, these AI engines. There are
new tools that we can leverage. The large language models, of course,
for getting all the headlines. That's not the only form of artificial intelligence out
there. But you know, I did have a thought as you were talking.
We're talking to Bob Muglia, who has a new book that is out
right now, very cool stuff. I took a look at it over the
weekend, the data preneurs, the promise of AI and the creators building our
future, and you know what I thought, that's quite interesting and ironic,
perhaps, Bob, is that this AI power is going to do two things
simultaneously. One, it's going to make, of course, deep fakes much
easier. But too, I think it is, if implemented properly, going
to affect much greater transparency. These are two different things, right, What
is real and what is fake are two different things. It's good at creating
the fake stuff, but I think it's also going to be good at generating
transparency such that we have trust in what we're looking at. What do you
think about that? Well, I think it's going to force us to work
together in a lot of different ways than we have ever worked before. It'll
do stuff it's going to do. I think in the short run, what
AI will do is it's going to help automate the types of tasks that people
don't really enjoy doing. And sometimes they're not all that good at doing because
they're repetitive tasks and people just get bored doing them. So we'll see a
lot of improvement in the way businesses work. I think almost every application will
change in the next three to five years to incorporate AI. Honestly, from
my perspective, it's the largest and most significant thing that's happened and technology in
my lifetime. I think the fact that all of a sudden we have computers
that can parse and really in some senses, understand English in any language,
not just English, any native language. Really they're quite good at all it's
quite good at all of them, and and be able to communicate with us
in a much more natural way. I think we finally have broken through and
we'll start to see the new applications coming out. Uh certainly in the productivity
space, in the space of office workers. I think that that scenario where
we didn't necessarily expect to see as much change, but because of the attributes
of these language models, people can be more effective. We've already seen it
improved developers and developer productivity markedly. I mean we getub is finding a forty
percent of their code that's that is checked in by people who are using getub
copilot are written by copilot. Well that's crazy. These are crazy improvements that
we just, you know, I wouldn't have expected and is now quite quite
available to people. Yeah, and that's a big deal. So I've been
reading about copilot, and you know, I've been seeing for in Google for
about two years now. I noticed when if you were in the Gmail client
just typing an email to someone, all of a sudden, recommendation would come
to finish your sentence. And I was like, Holy Christmas. That was
the early indicator, the early educator, right, And so Google has been
working on this, lots of people have been working on it. That of
course open AI rolls this out and that was the big Gong splash to kind
of wake the world up. But this is all sort of part and partial
to what you're calling the arc of data innovation, right. And I'm a
data guy. I've been a data guy for twenty three years in counting now
in terms of really focusing on the industry and as you kind of map the
evolution of functionality automation AI and now of course these large language models, you
can see it's it's very quickly becoming log arrhythmic in a way, right where
it goes like this and the petition and that's why the arc does that,
it goes a logarithmic. And that's a really interesting question, which is where
is it going to go in the next twenty years. If you look back,
I mean this didn't start. This didn't start a year or two ago.
I mean, this idea of building in assistance, this idea of using
some form of artificial intelligence, has been around for a long time and certainly
working on data and treating data as an important resource for organizations to work with.
I mean that goes back certainly to the nineteen to sixties for sure,
and the earliest databases that were created back then. But these pieces have built
on each other and there's been a long set of people that have helped make
it happen. You know, we saw the Internet come about and the transition
that that created, the fact that that text. I mean, when I
think about data, there's different kinds of data. There's structured data that you
might work with in a sequel database. There's semi structure data which takes which
is often stored in a format called jason and it's more hierarchical one. It's
used to report typically the operations that are done by internet and cloud systems.
But a very large amount of the data in the world isten the written word
in text, and we've seen how powerful that can be in working with these
large language models. But what I find just as interesting is that beyond text,
we're creating as a society a massive amount of information in other formats,
particularly video, but audio, and then of course taking text and making them
into more complicated documents that have precise meanings associated with them. All of these
things are complex sources of data. Sometimes people call audio or video unstructured data.
I prefer to call it complex data because that's really what it is.
It's not unstructured. There's lots of structure inside data. It's just that that
structure is so complicated that computers have not been able to understand. Now,
that's an interesting point. We could understand it as people, but computers couldn't.
If you took I mean, if you use you know, a digital
camera circuit two thousand and three, and you took a picture of a horse
eating hay in a barn. You could show that to a four year old
and they can identify what that picture is. It only it's only with an
advent, advent of these neural networks that have really come in since the early
twenty tens, in that period where we start to have algorithms that can begin
to identify and work with the contents of complex data. I mean, that's
where where we're getting things, and that's certainly where we things like see things
like stable diffusion and Dolly. All of those things are example of language models
that are also working with images, this interesting form of complex data. And
we'll see that in videos certainly shortly. Well, yeah, and of course
business needs are going to drive what gets done next, right, I mean,
I think we just saw with Facebook this huge experiment with the metaverse.
I looked that from the early days and thought, this is roadblocks for adults
or what's going on here exactly? And there were some very clever enterprise level
thoughts that went into this and how you could leverage this. But I think
it just got a bit off track and it wound up being a bit of
a learning experience for one of the biggest companies in the world. And it
goes to show you that you do need to have a business focus in mind.
When you leverage these technologies, you need to know what you're trying to
accomplish and then be realistic about tracking. I've thought not to get political in
any way, but I've thought to myself about in the political world, every
two years, we get to elect a new representative every four years, a
president every six years at center, it's a pretty long period of time to
go without having a check and balance. In the business world, we don't
get that kind of luxury. I mean, we can think we have two
years and then bad every quarter least, reporting every quarter at the very least,
and hopefully you're looking at things a lot more frequently than that. But
we do that too. That's another we got another show we'll talk about that.
I've got some ideas. We can get into the weeds about that,
but I think there is a good conversation to be had any tremendous value that
these algorithms can have. Look at healthcare, for example, and being able
to scan millions and millions of MRIs and detect patterns and ascertain different developments and
drug discoveries and things. It's a hue drug discovery. That's another example where
the ability to look at this and plus I mean the advancements that have made
in actually understanding protein folding, which for the first time we can, you
know, scientists and biologists can really understand how how some of these compounds will
work and how they will react when they're put inside a human body or any
kind of organism. It's it's it's a new world in terms of the types
of applications that can be built. I think one of the way I tend
to think about this is that that there's there's I sort of split this into
sort of two set of sets. There's there's a as a tool that people
will use, and then there's AI eventually and its evolution on its own and
where it's going, you know, how will it evolve and change over time.
We're that second one is still somewhat in the future. We're still in
the future because these ais are really not reached to the point where they are
continuously learning and continuously growing. They're much more batch oriented very early in their
incarnation. I do want, ever believe they're going to get smarter, and
they're going to continue to get smarter over time, and so we have to
think about how we will guide this AI as it continues to evolve and get
smarter, potentially getting smarter than we are, and and want that to be
We want that to be a cooperative agent in our society and to work with
us and together in the short run. The thing that's more interesting is how
will people use AI? And here the interesting observation I have is that is
that for every tool created since the beginning of time, mankind has used it
for every possible purpose, good, bad, and evil. Okay, And
people will first and foremost use AI for good. And there's a there's a
thousand, a million ways in which we can apply AI to society to help
people live their lives and lead more productive and better lives. There are also
ways where you can use AI for bad and for evil purposes. I mean,
amongst the most extreme of those are killer wrote the idea of building killer
robots that are essentially drones of some type that that potentially cooperate, work together,
and you know, go off and and and perform damage and kill people.
Those are to me, those are weapons of mass destruction. And we
need to think about it in that sense, and we need to make sure
that that that as people begin to use AI for nefarious purposes. For example,
spamming. I mean, that's an example where almost certainly we will see
AI being applied as smarter phishing sites and spamming sites, etc. But we'll
also see I applied to fight that too, which is the good news.
I mean, that's people. This is a case where people are using whatever
tools are at their disposal. But all people have these tools. And as
I say, you can use it for evil, but you can use it
for good, and you can also use it for preventative purposes. And AI
is an incredible tool in helping define spammers and discover these things and just shut
them down. And this is where I come back to This is where I
come back to you my sort of science fiction icon who I really consider to
be a prophet in so many ways, which is Isaac Asmoth and what he
was saying over fifty years ago, actually seventy years ago in the nineteen forties
and the nineteen fifties, when he envisioned a world of robotics. It was
Asthmoth that coined the term robotics. And the reason why that term is important
is because it's it's this, It's the term really means using using machines as
tools. Robot machines as tools for people. It's they are, they're engineered
to be used for people. And that's why Asmov invented the laws of robotics.
You know, the first law being a robot will not harm a human
or allow a human being to come to harm. The second law being that
a robot will behave you will will will will will follow a human being and
follow the orders of youan being as long as it doesn't violate the first law,
and the third law being it can protect. A robot can protect itself
as long as it doesn't violate the first two laws. And for today's world.
In today's world, I think that's very relevant because we should think about
how we build these next generation AI systems, which are kind of bots.
I mean, we're I see a new a new technology, a new generation
of search coming out that's really become what I would tend to call an answer
bot. Get a question that just gives you the answer. I find that
very very convenient. I personally, it's changed my search experience completely. Um
come agreed, uh And in the last three months, and what I know
is going to happen is by the end of this year that those answer bots
are going to turn into action bots where they'll do things for you, so
you know, you'll be able to say, hey, can you see if
there's a table at the you know, my favorite restaurant on Friday night at
seven o'clock and they'll say, well, but there's one at seven thirty,
and it's a great book. It for me, that's like a year away,
Okay, I mean I think we're not that far from that right now.
Um. And so to me, those are robots in a sense,
um, and it won't be long before they are more in our lives and
rolling around and having it and will be interacting them with them in ways that
are sort of as Mavvian in some senses. I mean, this was always
such a ridiculous piece of science fiction because as Mov had this idea that people
could talk to computers, which was absurd. It was just an absurd concept
until this last year. Well, it's now obvious that it's impossible to do
that, that's right. Well, yeah, I saw it just the other
day. I saw a news program and they were talking about the robots that
can dance and all these things, and the one guy says, well,
they can't talk to us, And I was like, uh, yeah,
they can. And the science, it turns out, the science though,
the physical sciences are moving very quickly. Um. You know, it was
a couple of years ago where Amazon was unable to The one job they were
unable to automate inside the warehouse was the picker, you know, which is
a complicated thing because these boxes are come robots literally delivered these boxes to humans
that have to choose whatever item is in it and it just randomly placed in
it. It could be anything in the box. The computer knows where it
is, so it brings the right box and the person has to pick it
out, and you know, and that was the job that could not be
automated. But that's getting you know, that level of grip and those sorts
of things are now are now starting to be to be to be achieved.
I think by the twenty thirties we'll start to see this, we'll start to
see that idea of robots on the street and even robots potentially in our households
and and it's a very interesting way of thinking about things if and to me,
there's a lot of potentially here. Asmov had some really nuanced views on
robotics. Most people, i mean a lot of people know about the three
laws, but most people don't know that there was actually a fourth law that
Asmov defined. He called it the Zeroth law, and in his later life
most of the laws were created in the nineteen forties when in his later life,
as he wrote some later Nova robot novels, the robots became much more
sophisticated, and we're almost they were almost agis. In fact, they were
agis. Wow. Well this one up after the break, folks, we're
talking about Muggy. All about artificial intelligence would be right back. You were
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to Inside Analysis. Here's your host, Eric Kabanov. All right, folks,
back here Inside Analysis with the legendary Bob Muglia, former CEO of Snowflake.
He's done a lot of boards, He's been doing a lot of cool
stuff for a long time now. He's a very cool fossil in his office
there in his home office that I've seen before. Crocodile. Yeah, oh
cool. They've been around a long time and I'm still here. You think
it's like one hundred and seventy million years old old? I as any to
say. I wish, I hope I could look that good at that age,
right exactly. Well, you're finishing a point, and then we want
to talk about this social contract, Go ahead, Yeah, and it's really
related to the social contract. You know. What I was talking about was
as these as these artificial intelligence, as it continues to get smarter, how
should we think about it? And here again, Asthmov gave us guidance,
you're not just for the short term, and how to how to work with
the agents that we have today. And we'll be coming the next few years,
but in the long run, as we begin to have artificial general intelligence
that is as smart as we are, or even becomes much much smarter than
we are over time. And that was as he recognized. In his latter
part of his career, he wrote some robot novels. His final robot novels,
he talked about robots who had essentially achieved a GI status and recognize that
the three laws were insufficient, and so they created the robots themselves created a
fourth law. Um they called it the Zeroth law as Malcutta Zeroth law,
which is that a robot must not harm humanity or allow humanity to come to
harm, putting it at a higher level, recognizing that that that they you
know that it's not just about an individual person and an individual encounter with somebody,
but it's really how does this device interact with society as a whole.
And certainly when we think now today. While I don't believe as Mu ever
thought about superintelligence the same way we did do today, his robots had positronic
brains in it that couldn't be replicated the way our digital brains can be.
They can be copied in a second, and then they can share informationtion instantly
as well. Um, that leads that has some interesting potential. Yet he
did give us guidance about how we need to create really a contract, a
social contract as people working with these devices that we're creating, recognizing that that
you know, we will want to coexist with them over a long period of
time. We do need to respect them and they need to respect us.
And we need to find ways where we can benefit over time and leverage these
tools that we've created to benefit society. And I think the potential is amazing
because we've got the reduction of the cost of intelligence together with knowledge. If
we can, if we can can continue to do things that make humanity more
and more productive, it just allows society to flourish in ways that it's never
flourished before. Yeah, and you brought up a couple interesting points throughout the
show about the ways that AI will help and I think again, in the
spirit of knowing how to use the tool effectively, you can use a tractor
effectively, or can use a tractor ineffectively. It's the same thing here,
and what we want to be able to do is help people understand how these
algorithms can solve for very mundane but serious problems like checking the books, for
example, auditing your books. I think before too long you're going to have
the option to just click a button say automate, and QuickBooks will go through.
Here's what we think your tax return should look like, and you say
yes or no and just let it go. That's coming pretty soon, I
think, because if you think about there are registries. Now if all the
different kinds of businesses in the country. This is a restaurant, that's a
bank, this is a nightclub, this is an office tower, for example,
that can be pretty easily along mind to forms on your schedule, see
for example, around your tax forms. I'm kind of surprising that has to
happen enough yet, but I think it's because it is such a serious task.
But think about how that just drives people crazy every year to do their
taxes. The irs, the irs IT systems are the worst on the plat.
Unfortunately, well there is that, there is government and so but back
to government, right, So, I've been a bing for transparency and governance,
and I lobbied for transparency and federal spending eighteen years ago, and amazingly
it happened, like good things happen, and they pass this act, the
Transparency Act of twenty thousand and five. But now I think we need transparency
of process and of systems so we can see the whole thing and then you
can see where the money goes. Because when people can see, there is
trust. If you can't see, if you have a black box, there's
not so much trust. What do you think? I totally agree. I
mean, I'm a huge fan of transparency. First of all, in creating
transparency and as we create these artificial intelligence agents that are working with us.
Is one of the reason why open source is such an important component in associated
with that, but very much in the solutions that get created as well.
Um, I'm a big believer, for example, that one of the biggest
things that can happen to reduce the cost of medical bills in this country is
total transparency of pricing. The only thing in our lives where which is a
consumer we purchase where we have no transparency in prices. Right, you buy
a house, you know what that costs, You buy a car, you
know what that costs. Anything you know the grocery store, but you have
no idea when you go into hospital what that's going to cost. And and
uh, and things like this can really help because it's not clear that that
that the outcomes match the cost structures. In fact, sometimes they're inversely related
to the costs. Right, So and again I think these agents can help
to u provide visibility to it. But it all comes down to people and
what people want. Um, I'm convinced there isn't transparency because they're the healthcare
system, because they're entities that don't want transparency. Yeah, there are forces
that don't want to and see. The point to be made here it seems
to me you've got just about three minutes left here is that the transparency will
come. I promise you you look at like the Pentagon papers for example,
or these various documents that get surfaced and get out there. Transparency will come
at some point. So will you be prepared for it? And I think
that's a bit of an impetus on the powers that be to get that ball
rolling. What do you think? Well, I mean, I think in
general we're in a world where the Internet has been the biggest impact on transparency
since the beginning of mankind. I think the ability to people see anything,
you can go anywhere you want. You don't like what this site is saying
go to another site, and I think AI will be like that as well.
AI will be another source that people can go to, another trusted source.
We're already, as I say, with these early answer bots that we're
starting to see, you know, being able to create trusted sources that people
can go to that are new and different. And and I think that that's
only going to expand over time and eneral that AI can open up society in
a way that it's never that it's never been. But again, it's all
going to come back to people and I and as I say, this is
a tool. AI is a tool. And when we talk about AI doing
this or AI doing that, remember it's the it's a person behind that AI
that's getting it to do what it's doing. They're not off doing something on
their own that may happen in ten years, but that's not happening now.
What's happening now is people are using them for everything, and as I say,
they'll use them for good, bad and evil. And it's up to
society to build the right level of governance. This idea of a social contract
that I think starts now but will continue to become progressively more important as AI
takes a bigger and bigger role in our lives. Yeah, that's right.
And folks, the book we're talking about is called The Data Preneurs, The
Promise of AI and the Creators Building Our Future by Bob Muglia and Steve hamp
So look that up on Amazon. We'll be sending out an email probably next
week with a link to all that. And I asked thought to close on
I mentioned that term black box, you know. I asked chat GBT which
database it used, and it demured it said, well, I'm not allowed
to tell you that, And that's not transparency, right. I mean,
I think that part of the legislation of the contract that we're going to get
here out of this and something will happen, really should be an emphasis on
transparency. Elon Musk, of course made part of the Twitter algorithm public he
open source that he's talked about going for total transparency. I think that is
of crucial importance because the feed. And I'd love to talk to you some
other time about this other topic of the media and getting trust in media and
getting accuracy and media reports and being able to kind of gauge things. We
have all these fact checkers, these days, But the feed used to come
from ABC and CBS and NBC. In the New York Times, it was
very static. It was either on your TV or in your newspaper. Then,
of course we had the bloggers. The Internet came along, and then
social media came in and sort of coopted that feed. But they control the
feed, so whether it's Facebook or Instagram or YouTube or whatever, they're in
control of that feed for their purposes, which of course is to make money
or gain influence in some fashion. I think transparency is going to be the
answer to save us from a lot of that stuff. What do you think?
I'm a big fan. I agree with that having multiple choices for people
is really important. I've personally believe that if all you're watching is one news
channel or getting reading one newspaper, you're only seeing part of the picture.
Is regardless of which one it is you're only seeing, you're only seeing half
or less of the picture, and you need to get news and information from
a wide variety of different sources. Fortunately, that's relatively easy to do today
that there are a wide variety of sources on the Internet, but people still
have to take it upon themselves to do it. Now, I may make
that easier over time, may help us to get a breadth of different perspectives.
I could spend an hour talking about about what truth is and this idea
suing truth because there are multiple truths in There are very few things that are
well. Some scientific things are generally regarded as true, but most things in
our society there is disagreement about and that has to get surfaced, and that
also must be surfaced to these AI bots so that they have that information as
they're forming their summaries and their opinions. I'm totally believe that the more information
that's out there and the more transparent we can make this, the more of
these bots that created, the better we are all off. I don't think
it should be held in the hands with the few people. I think it
needs to be available to society as a whole. That's right, the marketplace
of ideas, Like at all those ideas out there. Let the cream rise
to the top, folks. Look this book up online. Bob Lognia,
the Datapreneurs, the promise of AI, and the creators building our future.
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